DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical TextsCitation formats

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DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts. / Ananiadou, Sophia.

In: Bioinformatics (Oxford, England), 17.06.2020.

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@article{b90b4cee8798478ba73a87e8bddfe5cb,
title = "DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts",
abstract = "Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools.We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the Bidirectional Encoder Representations from Transformers (BERT) model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance.",
author = "Sophia Ananiadou",
year = "2020",
month = jun,
day = "17",
doi = "https://doi.org/10.1093/bioinformatics/btaa540",
language = "English",
journal = "Bioinformatics (Oxford, England)",
issn = "1367-4803",
publisher = "Oxford University Press",

}

RIS

TY - JOUR

T1 - DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts

AU - Ananiadou, Sophia

PY - 2020/6/17

Y1 - 2020/6/17

N2 - Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools.We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the Bidirectional Encoder Representations from Transformers (BERT) model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance.

AB - Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools.We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the Bidirectional Encoder Representations from Transformers (BERT) model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance.

U2 - https://doi.org/10.1093/bioinformatics/btaa540

DO - https://doi.org/10.1093/bioinformatics/btaa540

M3 - Article

JO - Bioinformatics (Oxford, England)

JF - Bioinformatics (Oxford, England)

SN - 1367-4803

ER -